class_docstr: Experimental class that wraps keepsake.init() and
  experiment.checkpoint() in a PyTorch Lightning callback.
  API is mostly consistent with keras_callback.KeepsakeCallback.
  
  This integration is subject to change, we'll probably
  try to get it into Pytorch Lightning itself as a logger.
  See https://github.com/replicate/keepsake/issues/432
  
  The KeepsakeCallback instantiates a new Keepsake experiment with
  keepsake.init on_pretrain_routine_start, using the parameters
  that are passed to the KeepsakeCallback constructor.
  
  Checkpoints are saved on_validation_end if validation is defined,
  otherwise on_epoch_end.
  
  For an example of how to use this callback, see the guide at
  https://keepsake.ai/docs/guides/pytorch-lightning-integration
code: def on_pretrain_routine_start(self, trainer, pl_module):
      self.experiment = keepsake.init(path=".", params=self.params)
  
file_path: keepsake/python/keepsake/pl_callback.py
imports: from copy import deepcopy
  from typing import Optional, Dict, Tuple, Any
  import keepsake
  from pytorch_lightning.callbacks.base import Callback
label_desc: Use image node_id 2 for calling the KeepsakeCallback obj's underlying member method code with example usage: obj.on_pretrain_routine_start(trainer, pl_module) without return types